Examensarbeten och uppsatser / Final Theses
Framläggningar på IDA / Presentations at IDA
Se även framläggningar annonserade hos ISY och ITN i Norrköping / See also presentations announced at ISY and ITN in Norrköping (in Swedish)
If nothing is stated about the presentation language then the presentation is in Swedish.
WExUpp - kommande framläggningar
2024-03-19 - SaS-UND |
Deep reinforcement learning for automated building climate control
Martin Hörnberg, Erik Snällfot
Avancerad (30hp)
kl 10:15, Charles Babbage (In English)
[Abstract]The building sector is the single largest contributor to greenhouse gas emissions, making it a natural focal point for reducing energy consumption. More efficient use of energy is also becoming increasingly important for property managers as global energy prices are skyrocketing. This report is conducted on behalf of Sustainable Intelligence, a Swedish company that specializes in building automation solutions. It investigates whether deep reinforcement learning algorithms can be implemented in a building control environment, if it can be more effective than traditional solutions, and if it can be achieved in reasonable time. The algorithms that were tested were Deep Deterministic Policy Gradient, DDPG, and Proximal Policy Optimization, PPO. They were implemented in a simulated BOPTEST environment in Brussels, Belgium, along with a traditional heating curve and a PI-controller for benchmarks. DDPG never converged, but PPO managed to reduce energy consumption compared to the best benchmark, while only having slightly worse thermal discomfort. The results indicate that DRL algorithms can be implemented in a building environment and reduce green house gas emissions in a reasonable training time. This might especially be interesting in a complex building where DRL can adapt and scale better than traditional solutions. Further research along with implementations on physical buildings need to be done in order to determine if DRL is the superior option. |
2024-03-20 - AIICS |
Approximation-based monitoring of ongoing model extraction attacks – model similarity tracking to assess the progress of an antagonist
Christian Gustavsson
Avancerad (30hp)
kl 08:30, Alan Turing (In English)
[Abstract]Many organizations turn to the promise of artificial intelligence and \acrfull{ML} as its use gains traction in many disciplines. However, developing high-performing ML models is often expensive. The design work can be complicated. Collecting large training datasets is often costly and can contain sensitive or proprietary information. For many reasons, machine learning models make for an appetizing target to an adversary interested in stealing data, model properties, or model behavior.
This work explores model extraction attacks and aims at designing an approximation-based monitor for tracking the progress of a potential adversary. When triggered, action can be taken to address the threat. The proposed monitor utilizes the interaction with a targeted model, continuously training a monitor model as a proxy for what the attacker could achieve, given the data gathered from the target.
The usefulness of the proposed monitoring approach is shown for two experimental attack scenarios. One explores the use of parametric and Bayesian models for a regression case, while the other explores commonly used neural network architectures for image classification.
The experiments expand current monitoring research to include ridge regression, Gaussian process regression, and a set of standard variants of convolutional neural networks: ResNet, VGG, and DenseNet. It also explores model and dataset similarity using metrics from statistical analysis, linear algebra, optimal transport, and a rank score. |
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Last updated: 2022-06-03